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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify the business value of generative AI solutions | 35–40% | - Selecting generative AI solutions for business needs - Differences between generative AI and other AI types - Business benefits, ROI, and efficiency gains - Foundational concepts of generative AI |
| Topic 2: Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35–40% | - Aligning Microsoft AI tools to business requirements - Microsoft 365 Copilot features and use cases - Azure AI services and Azure AI Foundry tools - Microsoft Copilot Studio capabilities |
| Topic 3: Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20–25% | - Responsible AI principles and governance - Organizational readiness and change management - Measuring success and continuous improvement - Adoption planning and scaling AI solutions |
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NEW QUESTION # 32
Your company uses a generative AI solution.
You need to improve the quality of responses by using grounding.
Which statement accurately describes how grounding improves accuracy and relevancy?
Answer: C
Explanation:
Grounding is a critical technique for improving the accuracy and relevance of generative AI solutions by linking or "anchoring" the large language model's (LLM) outputs to specific, verified, and up-to-date data sources. Without grounding, LLMs rely on their pre-trained, static, and often outdated knowledge, leading to "hallucinations"-confidently generated but incorrect, irrelevant, or fabricated information.
How Grounding Improves Accuracy and Relevance
Grounding transforms a general-purpose AI into a specialized, trustworthy, and actionable tool by providing the following benefits:
Reduces Hallucinations: By forcing the model to anchor its responses in provided data-such as internal documents, databases, or live web searches-grounding significantly reduces the likelihood of the model creating false information.
Enhances Contextual Relevance: Grounded models can access domain-specific, private data (e.g., CRM records, internal wikis, proprietary PDFs) rather than just public, general knowledge.
Ensures Data Freshness: Instead of relying on a static, old training cut-off date, grounding (often via Retrieval-Augmented Generation or RAG) enables the model to access the latest, real-time information, such as current inventory, updated policies, or recent news.
Provides Auditability and Trust: Grounded systems frequently provide citations or links to the exact source material used to generate the answer, allowing users to verify the information and increasing trust in the system.
Reference:
https://portkey.ai/blog/llm-grounding-for-accurate-outputs/
NEW QUESTION # 33
Your company is building a portfolio of AI-powered business solutions. Company executives want to understand how Microsoft responsible AI principles can support the company ' s long-term goals. Which benefit best demonstrates the importance of responsible AI? Select the BEST answer.
Answer: B
Explanation:
Responsible AI is fundamentally about earning and maintaining trust while scaling AI across the enterprise.
Option C is the best answer because responsible AI practices (fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness) reduce reputational, legal, and operational risk and make adoption sustainable over time. When stakeholders trust that AI is governed, tested, and monitored, the organization can expand AI usage confidently across business units.
The other options are incorrect because they make absolute or counterproductive claims. A is false:
responsible AI does not "guarantee" accuracy; it reduces risk and improves assurance, but no model can be guaranteed correct in all contexts. B is the opposite of reality: responsible AI increases the importance of data protection and governance; it does not reduce the need for them. D is also incorrect: responsible AI requires clear ownership and oversight, especially from leadership, because accountability is a core principle. In short, responsible AI matters because it builds stakeholder confidence and provides guardrails that support long- term, scalable, and compliant AI adoption-exactly what executives care about when investing in an AI portfolio.
NEW QUESTION # 34
Your company plans to implement a proof of concept (PoC) agent that uses Azure OpenAI. The solution must start small and provide flexibility to scale usage as demand grows. Which pricing model should you use?
Answer: A
Explanation:
The correct answer is B. Pay-as-you-go. A proof of concept should start with minimum commitment, flexible usage, and the ability to scale as demand becomes clearer. Pay-as-you-go pricing is appropriate because it allows the organization to pay based on actual model consumption rather than reserving dedicated capacity upfront. This is ideal when usage is uncertain, experimental, or expected to change during testing. Provisioned Throughput Units are better for predictable, high-volume production workloads that need reserved capacity and consistent throughput. Batch API is designed for asynchronous large-scale processing, not an interactive agent PoC. Microsoft 365 Copilot is a separate productivity service and not the Azure OpenAI pricing model for a custom agent solution.
NEW QUESTION # 35
Your company plans to use generative AI to help summarize and analyze internal business documents. You need to recommend a solution to prevent generative AI from accessing confidential or classified information.
What should you include in the recommendation?
Answer: B
Explanation:
Preventing generative AI from accessing confidential or classified information is primarily a data access and classification control problem. The most effective broad solution is data governance (B) -the framework of policies and controls that ensures sensitive content is identified, classified, protected, and access is restricted using least privilege. Data governance includes information classification/sensitivity labels, access control reviews, secure sharing practices, data loss prevention (DLP), and auditing-controls that directly limit what data is available to the AI through permission trimming and policy enforcement.
An information barrier (A) policy is more specific: it's intended to prevent communication and collaboration between defined user groups (often for regulatory/ethical walls). It does not comprehensively address document classification or restrict AI access to sensitive files across the tenant. A data retention policy (C) governs how long content is kept and when it's deleted; it's not a primary access-prevention mechanism. Communication monitoring (D) is a detection/oversight control; it can help identify risky activity, but it does not itself prevent access to confidential content by the AI.
Therefore, to prevent AI from surfacing confidential/classified data during summarization and analysis, you should recommend data governance as the primary control layer.
NEW QUESTION # 36
For each of the following statements, select Yes if the statement is true. Otherwise, select No . NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* A text-to-image generator can be used to translate content into other languages. Answer: No
* A predictive analytics model can generate new marketing content for a company's online ads. Answer:
No
* A generative AI chatbot can engage customers in personalized conversations and recommend products.Answer: Yes
* No - A text-to-image generator's primary function is to create images from text prompts , not translate text between languages. Translation is a natural language processing task typically handled by language models or dedicated translation services. A text-to-image model could illustrate translated content (e.g., generate an image based on a translated prompt), but it is not the tool used to perform the translation itself.
* No - Predictive analytics models are designed to predict outcomes (forecasts, probabilities, classifications) from historical patterns, such as predicting click-through rate, churn, or next-quarter demand. They are not designed to create new ad copy or marketing creatives. Generating new marketing content is a generative AI capability (text generation), not predictive analytics.
* Yes - A generative AI chatbot is well-suited to interactive, natural-language conversations . With access to product catalogs and business rules, it can ask clarifying questions, tailor responses to customer needs, and recommend products (for example, suggesting tents based on group size, season, and budget). This combines conversational generation with retrieval/recommendation logic behind the scenes, enabling personalized customer engagement at scale.
NEW QUESTION # 37
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